Julie Gershunskaya

U.S. Bureau of Labor Statistics | BLS · OEUS SMS

The mean field variational Bayes (VB) algorithm implemented in Stan is relatively fast and efficient, making it feasible to produce model-estimated official statistics on a rapid timeline. Yet, while consistent point estimates of parameters are achieved for continuous data models, the mean field approximation often produces inaccurate uncertainty q...

The recent proliferation of computers and the internet have opened new opportunities for collecting and processing data. However, such data are often obtained without a well-planned probability survey design. Such non-probability based samples cannot be automatically regarded as representative of the population of interest. Several classes of meth...

Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias s...

In this excellent overview of the history of probability and nonprobability sampling from the end of the nineteenth century to the present day, Professor Graham Kalton outlines the essence of past endeavors that helped to define philosophical approaches and stimulate the development of survey sampling methodologies. From the beginning, there was an...

We propose a novel Bayesian framework for the joint modeling of survey point and variance estimates for count data. The approach incorporates an induced prior distribution on the modeled true variance that sets it equal to the generating variance of the point estimate, a key property more readily achieved for continuous data response type models. O...

Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are performed using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias s...

Nonprobability (convenience) samples are increasingly sought to stabilize estimations for one or more population variables of interest that are performed using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the...

Government statistical agencies compose a population statistic for a given domain using a sample of units nested in that domain. Subsequent modeling of these domain survey estimates is often used to “borrow strength” across a dependence structure among the domains to improve estimation accuracy and efficiency. This paper focuses on models jointly d...

Small domain estimation models, like the Fay-Herriot (FH), often assume a normally distributed latent process centered on a linear mean function. The linearity assumption may be violated for domains that express idiosyncratic phenomena not captured by the predictors. The direct sample estimate for such domain will be viewed as an outlier by FH when...

The Current Employment Statistics (CES) survey, administered by the US Bureau of Labor Statistics, publishes total employment estimates for thousands of domains at detailed geographical and industrial levels. Some of these domains do not have adequate sample size for the direct probability sample-based estimates to be reliable. Small area estimatio...

We propose a new robust empirical best estimation approach to estimate small area finite population means that are relatively insensitive to a model misspecification or to the presence of outliers. This important robustness property is achieved by replacing the standard normality assumption of the sampling errors in a nested-error regression (NER)...

The Current Employment Statistics, administered by the U.S. Bureau of Labor Statistics, publishes total employment estimates for thousands of domains at detailed geographical and industrial levels. Some of these domains do not have adequate sample size for the direct probability sample-based estimates to be reliable. Small area estimation methods a...

Estimates from the Current Employment Statistics (CES) Survey are produced based on the data collected each month from the sample of businesses that is updated once a year. In some estimation cells, where the sample is not large enough, the Fay-Herriot model is used to improve the estimates. Under the current approach, the model combines informatio...

Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic tools for the evaluation of these patterns of variability. These tools are for (1) identification of...

Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth...

Estimates of employment from the Current Employment Statistics (CES) survey are published every month for a large number of cells defined at various detailed industrial and geographic levels. Before the estimates are released for publication, they need to be reviewed. The purpose of the review is to isolate cells that may contain erroneously report...

It is common for many establishment surveys that a sample contains a fraction of observations that may seriously affect survey estimates. Influential observations may appear in the sample due to imperfections of the survey design that cannot fully account for the dynamic and heterogeneous nature of the population of businesses. An observation may b...

Different methods have been proposed in the small area estimation literature to deal with outliers in individual observations and in the area-level random effects. In this paper, we propose a new method based on a scale mixture of two normal distributions. Using a simulation study, we compare the performance of a few recently proposed robust small...

The uncertainty associated with a survey estimate is commonly expressed in terms of its standard error estimate or a measure related to the standard error estimates such as estimated coefficient of variation or a confidence interval. For a linear survey estimator, the estimation of its design-based standard error for a simple probability sample des...

Each month, the Bureau of Labor Statistics publishes estimates of employment for industrial supersectors at the metropolitan statistical area (MSA) level. The survey-weighted ratio estimator that is used to produce estimates for large domains is generally less reliable for MSA level estimation due to the unavailability of adequate sample from a giv...

The U.S. Bureau of Labor Statistics (BLS) publishes monthly estimates of employment levels, one of the key indicators of the U.S. economy, for many domains. To assess the quality of these estimates, it is important to publish their associated standard error estimates. In our simulation study, the standard design- based variance estimators of the mo...

The Current Employment Statistics (CES) Survey uses a weighted link relative estimator to make estimates of employment at various levels of industry and area detail. The estimates are produced monthly approximately three weeks after the reference date of the survey. Sometimes outliers combined with relatively large probability weights result in inf...

For the Current Employment Statistics Program, approximately unbiased and stable variance estimators are important for the empirical evaluation of standard design-based point estimators, and for production of related small domain estimators. In some cases, standard design-based variance estimators can be relatively unstable, which may lead to consi...

ployment and Wages Program (ES-202), Design- based inference, Generalized least squares, Model- based inference, Superpopulation model, Variance estimator stability.

The Bureau of Labor Statistics has considerable interest in the estimation of total monthly employment for small domains defined by the intersection of metropolitan statistical areas (MSA) and major industrial divisions (MID), based on data from the Current Employment Statistics Survey (CES). One of several possible elementary estimators is a synth...